🌾 Built a Crop Recommendation System using Random Forest + Streamlit

To understand ML deployment in real-world scenarios, I built a crop recommender that predicts the most suitable crop based on soil nutrients and environmental conditions.

The core model is a Random Forest Classifier, trained on agricultural parameter data.

🔍 Features

• N, P, K based soil analysis • Temperature, Humidity & Rainfall inputs • Soil pH slider support • Instant prediction using trained model • Clean UI built with Streamlit • Model serialization using Pickle

⚙️ Tech Stack

Python | Scikit-learn | Random Forest | Pickle | Streamlit

🧠 How It Works

1️⃣ User enters soil & environmental parameters 2️⃣ Features are structured into a numeric vector 3️⃣ Random Forest model processes input 4️⃣ System predicts the most suitable crop 5️⃣ Result displayed instantly in the web interface

🎯 Why Random Forest?

• Handles non-linear relationships well • Works great with tabular structured data • Reduces overfitting compared to single Decision Trees • Provides strong baseline performance

🎯 Why I Built This

To understand:

• ML model training & selection • Deployment of serialized models • Converting ML scripts into interactive apps • Applying AI to agriculture use-cases

🚀 Next Steps

• Live weather API integration • Fertilizer recommendation engine • Feature importance visualization • Prediction logging with database • Dockerized deployment

project GitHub repo :> https://github.com/glitchyguy101/Crop-predictor.git